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R Package clickstream: Analyzing Clickstream Data with Markov Chains

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  • Scholz, Michael

Abstract

Clickstream analysis is a useful tool for investigating consumer behavior, market research and software testing. I present the clickstream package which provides functionality for reading, clustering, analyzing and writing clickstreams in R. The package allows for a modeling of lists of clickstreams as zero-, first- and higher-order Markov chains. I illustrate the application of clickstream for a list of representative clickstreams from an online store.

Suggested Citation

  • Scholz, Michael, 2016. "R Package clickstream: Analyzing Clickstream Data with Markov Chains," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i04).
    • Handle: RePEc:jss:jstsof:v:074:i04
    DOI: http://hdl.handle.net/10.18637/jss.v074.i04
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    References listed on IDEAS

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    1. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    3. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
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    Cited by:

    1. Cristian Preda & Quentin Grimonprez & Vincent Vandewalle, 2021. "Categorical Functional Data Analysis. The cfda R Package," Mathematics, MDPI, vol. 9(23), pages 1-31, November.
    2. Gautam Pal & Katie Atkinson & Gangmin Li, 2023. "Real-time user clickstream behavior analysis based on apache storm streaming," Electronic Commerce Research, Springer, vol. 23(3), pages 1829-1859, September.
    3. Pavlos Delias & Vassilios Zoumpoulidis & Ioannis Kazanidis, 2019. "Visualizing and exploring event databases: a methodology to benefit from process analytics," Operational Research, Springer, vol. 19(4), pages 887-908, December.

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